Arbeitspapier
Robust estimation of dimension reduction space
Most dimension reduction methods based on nonparametric smoothing are highly sensitive to outliers and to data coming from heavy-tailed distributions. We show that the recently proposed methods by Xia et al. (2002) can be made robust in such a way that preserves all advantages of the original approach. Their extension based on the local one-step M-estimators is sufficiently robust to outliers and data from heavy tailed distributions, it is relatively easy to implement, and surprisingly, it performs as well as the original methods when applied to normally distributed data.
- Sprache
-
Englisch
- Erschienen in
-
Series: SFB 649 Discussion Paper ; No. 2005,015
- Klassifikation
-
Wirtschaft
- Thema
-
Dimension reduction
Nonparametric regression
M-estimation
Nichtparametrisches Verfahren
Robustes Verfahren
Schätztheorie
Theorie
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Čίžek, Pavel
Härdle, Wolfgang Karl
- Ereignis
-
Veröffentlichung
- (wer)
-
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
- (wo)
-
Berlin
- (wann)
-
2005
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Čίžek, Pavel
- Härdle, Wolfgang Karl
- Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
Entstanden
- 2005
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